Konstantin  Kutzkov

kutzkov_at_gmail_com

Welcome to my website. My name is Konstantin Kutzkov and I am a machine learning researcher. Most recently, my research interests have been in the area of graph machine learning.

I obtained my PhD from ITU Copenhagen under the supervision of Rasmus Pagh on algorithmic techniques for data summarization. My experience includes work in academia and industry, you can visit my Linkedin profile for more details on my education and professional experience. You can find my research papers below.  Also, I am describing the main ideas behind my research in blog posts intended for a more general audience, see my Medium profile.

 

Journal publications

Laurent Bulteau, Vincent Froese, Konstantin Kutzkov, Rasmus Pagh.
Triangle Counting in Dynamic Graph Streams
Algorithmica, 76(1), 259–278, 2016.

Alexander Golovnev, Konstantin Kutzkov.
New Exact Algorithms for the 2-Constraint Satisfaction Problem.
Theoretical Computer Science 526, 18--27, 2014

Konstantin Kutzkov.
An Exact Exponential Time Algorithm for Counting Bipartite Cliques.
Information Processing Letters 112(13), 535--539, 2012

Konstantin Kutzkov.
New Upper Bound for the #3-SAT Problem.
Information Processing Letters 105(1), 1--5, 2007

 

Conference proceedings

Konstantin Kutzkov.
LoNe Sampler: Graph node embeddings by coordinated local neighborhood sampling.
The 37th AAAI Conference on Artificial Intelligence (AAAI), 2023: 8413-8420.
Code

Francesco Bonchi, David Garcia-Soriano, Konstantin Kutzkov, Charalampos E. Tsourakakis.
Query-Efficient Correlation Clustering.
The Web Conference (WWW), 2020: 1468--1478

Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic.
KONG: Kernels for Ordered Neighborhood Graphs.
32nd Conference on Neural Information Processing Systems (NeurIPS), 2018: 4055--4064
Code

Tian Guo, Konstantin Kutzkov, Mohamed Ahmed, Jean-Paul Calbimonte, Karl Aberer.
Efficient Distributed Decision Trees for Robust Regression.
European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD),~2016: 79--95

Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.
Learning Convolutional Neural Networks for Graphs.
33rd International Conference on Machine Learning (ICML), 2016: 2014-2023

Konstantin Kutzkov, Mohamed Ahmed, Sofia Nikitaki.
Weighted Similarity Estimation in Data Streams.
24th ACM International Conference on Information and Knowledge Management (CIKM), 2015: 1051-1060

David Garcia-Soriano, Konstantin Kutzkov.
Triangle Counting in Streamed Graphs via Small Vertex Covers.
14th SIAM International Conference on Data Mining (SDM), 2014: 352--360

Konstantin Kutzkov, Rasmus Pagh.
Triangle Counting in Dynamic Graph Streams.
14th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT), 2014: 306--318

Konstantin Kutzkov, Rasmus Pagh.
Consistent Subset Sampling.
14th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT), 2014: 294--305

Konstantin Kutzkov, Albert Bifet, Francesco Bonchi, Aristides Gionis.
STRIP: Stream Learning of Influence Probabilities.
19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013: 275-283

Konstantin Kutzkov.
Deterministic Algorithms for Skewed Matrix Products.
30th International Symposium on Theoretical Aspects of Computer Science (STACS), 2013: 466-477

Konstantin Kutzkov, Rasmus Pagh.
On the Streaming Complexity of Computing Local Clustering Coefficients.
6th ACM conference on Web Search and Data Mining (WSDM), 2013: 677-686

Andrea Campagna, Konstantin Kutzkov, Rasmus Pagh.
On Parallelizing Matrix Multiplication by the Column-Row Method.
15th Meeting on Algorithm Engineering and Experiments (ALENEX), 2013: 122-132

Konstantin Kutzkov.
Improved Counter Based Algorithms for Frequent Pairs Mining in Transactional Data Streams.
European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2012: 843-858

Alexander S. Kulikov, Konstantin Kutzkov.
New Bounds for MAX-SAT by Clause Learning.
Second International Symposium on Computer Science in Russia (CSR), 2007: 194-204

Note that the above papers are provided solely for educational purposes. The copyright belongs to the respective publisher.